where \(f_{e}\)  denotes the instance-level tracking embedding of \(\Omega _{i}\) 
and \(N_{i}\)  denotes the number of
locations on the feature maps belong to
\(\Omega _{i}\) 
with classification score \(>\)  0.05 [1]}.
To encourage closer embeddings from the same instance, we adopt the center loss to minimize the L1 distance:
\(L_{i}^{center} \ = \ \sum _{e\in \Omega _{i} }^{} \parallel C_{i} - f_{e} \parallel \ .\) 
